Socially Assistive Robots in Elderly Care: A Mixed-Method Systematic Literature Review

By Reza Kachouie, Sima Sedighadeli, Rajiv Khosla, Mei-Tai Chu. International journal of human computer interactions, 2014

399 citations recorded by the source corpus.

The world’s population is aging, and developed countries are engaged in developing a new aged-care paradigm to reduce spiraling healthcare costs. Assistive technologies like Socially Assistive Robots (SAR) are being considered as enablers to support the process of care giving or keep elderly at home longer. This article reports a mixed-method systematic review of SAR in elderly care and recognizes its impact on elderly well-being, integrating evidence from qualitative and quantitative studies. It follows the principles explained in Cochrane Handbook for Systematic Reviews of Interventions and classifies interventions, measures, and outcomes of field trials of SAR in elderly care. Eighty-six studies in 37 study groups have been included. The findings imply positive effects of SAR on elderly well-being. Ten significant recommendations are made to help avoid the current limitations of existing research and to improve future research and its applicability. This review revealed that SAR can potentially enhance elderly well-being and decrease the workload on caregivers. There is a need for rigorous research methodology, person-centered care, caregiver expectation model, multimodal interaction, multimodal data collection, and modeling of culturally diverse groups to facilitate acceptability of SAR.

Subfields: Demography, Occupational Therapy, Social Psychology

View Paper: https://www.semanticscholar.org/paper/3e9bd2ece97b74905a93de395df66c1c91a3405d

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In Social Psychology

Citations in the corpus

IoT-Enabled Social Relationships Meet Artificial Social Intelligence (2021, neutral): “With the help of ASI, the elderly are surrounded by socialaware machines embedded with human-like personality traits designed especially to be harmonic with the aged user’s personality traits [90].”
Self-organizing neural integration of pose-motion features for human action recognition (2015, background): “The robust classiﬁcation of full-body, articulated actions represents a key component of assistive robots aiming to provide reliable recognition of user behavior and remains an enticing milestone for artiﬁcial systems embedded in socially-aware agents (Kachouie et al., 2014).”
Seven HCI Grand Challenges (2019, background): “Future efforts should focus on person-centered care, by improving personalization of care, and considering different needs, expectations, and preferences of individuals (Kachouie, Sedighadeli, Khosla, & Chu, 2014).”
A Systematic Review of Attitudes, Anxiety, Acceptance, and Trust Towards Social Robots (2020, neutral): “Finally, we attempted to analyse the effect of previous experience with robots on participants’ attitudes, as research has found that this might play a role in shaping people’s beliefs about robots [54, 84, 85].”
Human motion assessment in real time using recurrent self-organization (2016, background): “In this context, the correctness of postural transitions is paramount during the execution of welldefined physical routines, since inaccurate movements may significantly reduce the overall efficiency of the movement and increase the risk of injury [1].”